Sentiment-Aware Fuzzy Clustering Model for X Social Media Behavior Analysis

Abstract

Social media sites such as Twitter (X) changed into high-velocity observatories of collective mood with millions of short, informal utterances recording public reactions to events, products, policies, and cultural moments in near real time.  Mining the stream for actionable insight calls for approaches that respect two notoriously hard to handle properties of social text: (i) ambiguity — in reality, most posts display mixed or low-intensity affect rather than a single discrete label; and (ii) contextual drift — lexical and topical signals co-evolve with communities and time. Many standard sentiment pipelines requiring each message to be labeled as a single discrete class (positive/negative/neutral) fail to provide adequate behavioral insights for crisis monitoring or policy assessment. We close this gap by presenting a Sentiment-Aware Fuzzy Clustering model that models sentiment as a continuous signal and community mood as overlapping regions instead of disjoint boxes. We assign a polarity score to each post and then discretize the space using Fuzzy C-Means (FCM) to assign partial memberships to a number of emotional groups. This uncertainty-aware representation is more reflective of actual online behavior (i.e. a post could be strongly positive but still exhibit features of the neutral discourse) and serves as a principled basis for downstream interpretation in population scale.